David B. Dunson
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Faculty Network Member of the Duke Institute for Brain Sciences
Development of Bayesian statistical methods and approaches for uncertainty quantification motivated by applications with complex and high-dimensional data. A particular interest is in high-dimensional low sample size data in which it is necessary to incorporate dimensional reduction through carefully designed prior distributions and challenges arise in efficiently computing posterior approximations. Ongoing focus areas include new algorithms for approximating posterior distributions in big data settings, nonparametric Bayes probability modeling allowing for uncertainty in distributional assumptions, analysis of network data, incorporating physical and geometric prior knowledge in modeling and novel models for dimension reduction for "object data" (functions, tensors, shapes, etc). Primary application areas include genomics, neurosciences, epidemiology, and reproductive studies but with much broader interests in developing new methods motivated by difficult applications (in art, music, radar, imaging processing, etc).
Rao, V, Lin, L, and Dunson, DB. "Data augmentation for models based on rejection sampling." Biometrika 103.2 (June 2016): 319-335. Full Text Open Access Copy
Guhaniyogi, R, and Dunson, DB. "Compressed Gaussian process for manifold regression." Journal of Machine Learning Research 17 (May 1, 2016).
Ovaskainen, O, Abrego, N, Halme, P, and Dunson, D. "Using latent variable models to identify large networks of species-to-species associations at different spatial scales." Ed. D Warton. Methods in Ecology and Evolution 7.5 (May 2016): 549-555. Full Text
Kabisa, ST, Dunson, DB, and Morris, JS. "Online Variational Bayes Inference for High-Dimensional Correlated Data." Journal of Computational and Graphical Statistics 25.2 (April 2, 2016): 426-444. Full Text
Yang, Y, and Dunson, DB. "Bayesian Conditional Tensor Factorizations for High-Dimensional Classification." Journal of the American Statistical Association 111.514 (April 2, 2016): 656-669. Full Text
Zhou, J, Herring, AH, Bhattacharya, A, Olshan, AF, Dunson, DB, and National Birth Defects Prevention Study, . "Nonparametric Bayes modeling for case control studies with many predictors." Biometrics 72.1 (March 2016): 184-192. Full Text
Tang, K, Dunson, DB, Su, Z, Liu, R, Zhang, J, and Dong, J. "Subspace segmentation by dense block and sparse representation." Neural networks : the official journal of the International Neural Network Society 75 (March 2016): 66-76. Full Text
Kunihama, T, and Dunson, DB. "Nonparametric Bayes inference on conditional independence." Biometrika 103.1 (March 2016): 35-47. Full Text